File size: 50,419 Bytes
fb8a87c b44701d fb8a87c b44701d fb8a87c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 | from __future__ import annotations
import io
import os
import re
from dataclasses import asdict, dataclass
from pathlib import Path
from subprocess import check_output
from tempfile import TemporaryDirectory
from typing import TYPE_CHECKING, Any
import biotite.structure as bs
import biotite.structure.io.pdbx as pdbx
import brotli
import msgpack
import numpy as np
import torch
from biotite.structure.io.pdbx import (
CIFCategory,
CIFColumn,
CIFData,
CIFFile,
set_structure,
)
from . import esmfold2_residue_constants as residue_constants
from .esmfold2_metrics import compute_lddt, compute_rmsd
from .esmfold2_protein_complex import ProteinComplex, ProteinComplexMetadata
@dataclass
class MolecularComplexResult:
"""Result of molecular complex folding"""
complex: MolecularComplex
plddt: torch.Tensor | None = None
ptm: float | None = None
iptm: float | None = None
pae: torch.Tensor | None = None
distogram: torch.Tensor | None = None
pair_chains_iptm: torch.Tensor | None = None
output_embedding_sequence: torch.Tensor | None = None
output_embedding_pair_pooled: torch.Tensor | None = None
residue_index: torch.Tensor | None = None
entity_id: torch.Tensor | None = None
sae_features: np.ndarray | None = None # [L, n_features]
ttt_metrics: dict[str, Any] | None = None
@dataclass
class MolecularComplexMetadata:
"""Metadata for MolecularComplex objects."""
entity_lookup: dict[int, str]
chain_lookup: dict[int, str]
assembly_composition: dict[str, list[str]] | None = None
@dataclass
class Molecule:
"""Represents a single molecule/token within a MolecularComplex."""
token: str
token_idx: int
atom_positions: np.ndarray # [N_atoms, 3]
atom_elements: np.ndarray # [N_atoms] element strings
atom_names: np.ndarray | None = None # [N_atoms] atom names (optional)
atom_hetero: np.ndarray | None = None # [N_atoms] hetero flags (optional)
residue_type: int = 0
molecule_type: int = 0 # PROTEIN=0, RNA=1, DNA=2, LIGAND=3
confidence: float = 0.0
@dataclass(frozen=True)
class MolecularComplex:
"""
Dataclass representing a molecular complex with support for proteins, nucleic acids, and ligands.
Uses a flat atom representation with token-based sequence indexing, supporting all atom types
beyond the traditional atom37 protein representation.
"""
id: str
sequence: list[str] # Token sequence like ['MET', 'LYS', 'A', 'G', 'ATP']
# Flat atom arrays - simplified representation
atom_positions: np.ndarray # [N_atoms, 3] 3D coordinates
atom_elements: np.ndarray # [N_atoms] element strings
# Token-to-atom mapping for efficient access
token_to_atoms: np.ndarray # [N_tokens, 2] start/end indices into atoms array
# Chain information
chain_id: np.ndarray # [N_tokens] chain identifier for each token
# Confidence data
plddt: np.ndarray # Per-token confidence scores [N_tokens]
# Metadata
metadata: MolecularComplexMetadata
# Optional atom names and hetero flags (preserved from original structures)
atom_names: np.ndarray | None = None # [N_atoms] atom names (optional)
atom_hetero: np.ndarray | None = None # [N_atoms] hetero flags (optional)
def __post_init__(self):
"""Validate array dimensions."""
n_tokens = len(self.sequence)
n_atoms = len(self.atom_positions)
assert (
self.token_to_atoms.shape[0] == n_tokens
), f"token_to_atoms shape {self.token_to_atoms.shape} != {n_tokens} tokens"
assert (
self.chain_id.shape[0] == n_tokens
), f"chain_id shape {self.chain_id.shape} != {n_tokens} tokens"
assert (
self.plddt.shape[0] == n_tokens
), f"plddt shape {self.plddt.shape} != {n_tokens} tokens"
if self.atom_names is not None:
assert (
self.atom_names.shape[0] == n_atoms
), f"atom_names shape {self.atom_names.shape} != {n_atoms} atoms"
if self.atom_hetero is not None:
assert (
self.atom_hetero.shape[0] == n_atoms
), f"atom_hetero shape {self.atom_hetero.shape} != {n_atoms} atoms"
def __len__(self) -> int:
"""Return number of tokens."""
return len(self.sequence)
def __getitem__(self, idx: int) -> Molecule:
"""Access individual molecules/tokens by index."""
if idx >= len(self.sequence) or idx < 0:
raise IndexError(
f"Token index {idx} out of range for {len(self.sequence)} tokens"
)
token = self.sequence[idx]
start_atom, end_atom = self.token_to_atoms[idx]
# Extract atom data for this token
token_atom_positions = self.atom_positions[start_atom:end_atom]
token_atom_elements = self.atom_elements[start_atom:end_atom]
token_atom_names = None
if self.atom_names is not None:
token_atom_names = self.atom_names[start_atom:end_atom]
token_atom_hetero = None
if self.atom_hetero is not None:
token_atom_hetero = self.atom_hetero[start_atom:end_atom]
# Default values for residue/molecule type (would be extended based on actual implementation)
residue_type = 0 # Default to standard residue
molecule_type = 0 # Default to protein
return Molecule(
token=token,
token_idx=idx,
atom_positions=token_atom_positions,
atom_elements=token_atom_elements,
atom_names=token_atom_names,
atom_hetero=token_atom_hetero,
residue_type=residue_type,
molecule_type=molecule_type,
confidence=self.plddt[idx],
)
@property
def atom_coordinates(self) -> np.ndarray:
"""Get flat array of all atom coordinates [N_atoms, 3]."""
return self.atom_positions
# Conversion methods
@classmethod
def from_protein_complex(cls, pc: ProteinComplex) -> "MolecularComplex":
"""Convert a ProteinComplex to MolecularComplex.
Args:
pc: ProteinComplex object with atom37 representation
Returns:
MolecularComplex with flat atom arrays and token-based indexing
"""
from . import esmfold2_residue_constants
# Extract sequence without chain breaks
sequence_no_breaks = pc.sequence.replace("|", "")
sequence_tokens = [
residue_constants.restype_1to3.get(aa, "UNK") for aa in sequence_no_breaks
]
# Convert atom37 to flat arrays
flat_positions = []
flat_elements = []
flat_names = []
flat_hetero = []
token_to_atoms = []
atom_idx = 0
for i, aa in enumerate(pc.sequence):
if aa == "|":
# Skip chain break tokens
continue
# Get atom37 positions and mask for this residue.
# ProteinComplex arrays are indexed by sequence position (including |),
# so use `i` not a separate residue counter.
res_positions = pc.atom37_positions[i] # [37, 3]
res_mask = pc.atom37_mask[i] # [37]
# Track start position for this token
token_start = atom_idx
# Process each atom type in atom37 representation
for atom_type_idx, atom_name in enumerate(residue_constants.atom_types):
if res_mask[atom_type_idx]: # Atom is present
# Add position
flat_positions.append(res_positions[atom_type_idx])
# Determine element from atom name
element = (
atom_name[0] if atom_name else "C"
) # First character is element
flat_elements.append(element)
# Add atom name
flat_names.append(atom_name)
# Add hetero flag (all proteins are non-hetero)
flat_hetero.append(False)
atom_idx += 1
# Record token-to-atom mapping [start_idx, end_idx)
token_to_atoms.append([token_start, atom_idx])
# Convert to numpy arrays
atom_positions = np.array(flat_positions, dtype=np.float32)
atom_elements = np.array(flat_elements, dtype=object)
atom_names = np.array(flat_names, dtype=object)
atom_hetero = np.array(flat_hetero, dtype=bool)
token_to_atoms_array = np.array(token_to_atoms, dtype=np.int32)
# Extract confidence scores and chain_ids (skip chain breaks)
confidence_scores = []
chain_ids = []
for seq_idx, aa in enumerate(pc.sequence):
if aa != "|":
confidence_scores.append(pc.confidence[seq_idx])
chain_ids.append(pc.chain_id[seq_idx])
confidence_array = np.array(confidence_scores, dtype=np.float32)
chain_id_array = np.array(chain_ids, dtype=np.int64)
# Create metadata - convert entity IDs to strings for MolecularComplexMetadata
entity_lookup_str = {k: str(v) for k, v in pc.metadata.entity_lookup.items()}
metadata = MolecularComplexMetadata(
entity_lookup=entity_lookup_str,
chain_lookup=pc.metadata.chain_lookup,
assembly_composition=pc.metadata.assembly_composition,
)
return cls(
id=pc.id,
sequence=sequence_tokens,
atom_positions=atom_positions,
atom_elements=atom_elements,
token_to_atoms=token_to_atoms_array,
chain_id=chain_id_array,
plddt=confidence_array,
metadata=metadata,
atom_names=atom_names,
atom_hetero=atom_hetero,
)
def to_protein_complex(self) -> ProteinComplex:
"""Convert MolecularComplex back to ProteinComplex format.
Extracts only protein tokens and converts from flat atom representation
back to atom37 format used by ProteinComplex.
Returns:
ProteinComplex with protein residues only, excluding ligands/nucleic acids
"""
from . import esmfold2_residue_constants
# No need for element mapping - already using element characters
# Filter for protein tokens only (skip ligands, nucleic acids)
protein_tokens = []
protein_indices = []
for i, token in enumerate(self.sequence):
# Check if token is a standard 3-letter amino acid code
if token in residue_constants.restype_3to1:
protein_tokens.append(token)
protein_indices.append(i)
if not protein_tokens:
raise ValueError("No protein tokens found in MolecularComplex")
n_residues = len(protein_tokens)
# Initialize atom37 arrays
atom37_positions = np.full((n_residues, 37, 3), np.nan, dtype=np.float32)
atom37_mask = np.zeros((n_residues, 37), dtype=bool)
# Extract confidence scores and chain_ids for protein residues only
protein_confidence = self.plddt[protein_indices]
protein_chain_ids = self.chain_id[protein_indices]
# Convert tokens back to single-letter sequence with chain breaks
single_letter_residues = []
prev_chain_id = None
for i, (token, chain_id_val) in enumerate(
zip(protein_tokens, protein_chain_ids)
):
# Add chain break if we're switching to a new chain
if prev_chain_id is not None and chain_id_val != prev_chain_id:
single_letter_residues.append("|")
single_letter_residues.append(residue_constants.restype_3to1[token])
prev_chain_id = chain_id_val
single_letter_sequence = "".join(single_letter_residues)
# Calculate final sequence length (includes chain breaks)
sequence_length = len(single_letter_sequence)
# Convert flat atoms back to atom37 representation using atom names
for res_idx, token_idx in enumerate(protein_indices):
token = self.sequence[token_idx]
start_atom, end_atom = self.token_to_atoms[token_idx]
res_atom_positions = self.atom_positions[start_atom:end_atom]
res_atom_names = (
np.array(self.atom_names[start_atom:end_atom], dtype=str)
if self.atom_names is not None
else np.array([], dtype=str)
)
# Build a mapping from normalized atom name -> position for this residue
# Normalize to uppercase and strip whitespace for robust matching
name_to_pos: dict[str, np.ndarray] = {}
for i, nm in enumerate(res_atom_names):
key = nm.upper().strip()
# Prefer first occurrence; ignore duplicates/altlocs
if key not in name_to_pos:
name_to_pos[key] = res_atom_positions[i]
# Place atoms into atom37 by matching stored atom names to atom37 indices.
# This handles all atoms present in the flat representation, not just
# the canonical residue_atoms for this residue type. This preserves
# atoms that were in the original atom37_mask even if they're atypical
# for the residue (e.g., from alternate conformations or data quirks).
for atom_name_str, pos in name_to_pos.items():
idx37 = residue_constants.atom_order.get(atom_name_str)
if idx37 is not None:
atom37_positions[res_idx, idx37] = pos
atom37_mask[res_idx, idx37] = True
# Create arrays that match sequence length (including chain breaks)
# Initialize arrays with proper size
chain_id_expanded = np.full(sequence_length, -1, dtype=np.int64)
entity_id_expanded = np.full(sequence_length, -1, dtype=np.int64)
sym_id_expanded = np.zeros(sequence_length, dtype=np.int64)
residue_index_expanded = np.zeros(sequence_length, dtype=np.int64)
insertion_code_expanded = np.array([""] * sequence_length, dtype=object)
confidence_expanded = np.zeros(sequence_length, dtype=np.float32)
atom37_positions_expanded = np.full(
(sequence_length, 37, 3), np.nan, dtype=np.float32
)
atom37_mask_expanded = np.zeros((sequence_length, 37), dtype=bool)
# Map residue data to sequence positions (skipping chain breaks)
residue_idx = 0
residue_counter_per_chain = {}
for seq_pos, char in enumerate(single_letter_sequence):
if char != "|":
# This is a residue position
chain_id_val = protein_chain_ids[residue_idx]
chain_id_expanded[seq_pos] = chain_id_val
entity_id_expanded[seq_pos] = chain_id_val # Simplified mapping
# Track residue numbering per chain
if chain_id_val not in residue_counter_per_chain:
residue_counter_per_chain[chain_id_val] = 1
else:
residue_counter_per_chain[chain_id_val] += 1
residue_index_expanded[seq_pos] = residue_counter_per_chain[
chain_id_val
]
confidence_expanded[seq_pos] = protein_confidence[residue_idx]
atom37_positions_expanded[seq_pos] = atom37_positions[residue_idx]
atom37_mask_expanded[seq_pos] = atom37_mask[residue_idx]
residue_idx += 1
# Chain break positions keep default values (-1, False, etc.)
# Use the expanded arrays
chain_id = chain_id_expanded
entity_id = entity_id_expanded
sym_id = sym_id_expanded
residue_index = residue_index_expanded
insertion_code = insertion_code_expanded
protein_confidence = confidence_expanded
atom37_positions = atom37_positions_expanded
atom37_mask = atom37_mask_expanded
# Create protein complex metadata preserving chain information
# Convert MolecularComplex metadata to ProteinComplex format
unique_chain_ids = np.unique(protein_chain_ids)
entity_lookup = {int(cid): int(cid) for cid in unique_chain_ids}
chain_lookup = {
int(cid): self.metadata.chain_lookup.get(int(cid), chr(65 + int(cid)))
for cid in unique_chain_ids
}
protein_metadata = ProteinComplexMetadata(
entity_lookup=entity_lookup,
chain_lookup=chain_lookup,
assembly_composition=self.metadata.assembly_composition,
)
return ProteinComplex(
id=self.id,
sequence=single_letter_sequence,
entity_id=entity_id,
chain_id=chain_id,
sym_id=sym_id,
residue_index=residue_index,
insertion_code=insertion_code,
atom37_positions=atom37_positions,
atom37_mask=atom37_mask,
confidence=protein_confidence,
metadata=protein_metadata,
)
@classmethod
def from_mmcif(cls, inp: str, id: str | None = None) -> "MolecularComplex":
"""Read MolecularComplex from mmcif file or string.
Args:
inp: Path to mmCIF file or mmCIF content as string
id: Optional identifier to assign to the complex
Returns:
MolecularComplex with all molecules (proteins, ligands, nucleic acids)
"""
from io import StringIO
# Check if input is a file path or mmCIF string content
if os.path.exists(inp):
# Input is a file path
mmcif_file = pdbx.CIFFile.read(inp)
else:
# Input is mmCIF string content
mmcif_file = pdbx.CIFFile.read(StringIO(inp))
# Get structure - handle missing model information gracefully
try:
structure = pdbx.get_structure(
mmcif_file, model=1, extra_fields=["b_factor"]
)
except (KeyError, ValueError):
# Fallback for mmCIF files without model information
try:
structure = pdbx.get_structure(mmcif_file)
except Exception:
# Last resort: use the first available model or all atoms
structure = pdbx.get_structure(mmcif_file, model=None)
# Type hint for pyright - structure is an AtomArray which is iterable
if TYPE_CHECKING:
structure: Any = structure
# Read label_asym_id from the raw CIF atom_site category.
# Biotite's atom.chain_id uses auth_asym_id, which collapses ligands
# onto their parent protein chain. label_asym_id gives each entity a
# distinct chain identifier.
block = mmcif_file.block
label_asym_ids: list[str] | None = None
if "atom_site" in block:
atom_site = block["atom_site"]
if "label_asym_id" in atom_site:
_col = atom_site["label_asym_id"]
_raw = (
_col.as_array(str)
if hasattr(_col, "as_array")
else np.array(list(_col), dtype=str) # type: ignore[arg-type]
)
# biotite's get_structure(model=1) filters to model 1 AND
# removes alternate conformations. We must apply the same
# filters to label_asym_id to keep arrays aligned.
keep = np.ones(len(_raw), dtype=bool)
if "pdbx_PDB_model_num" in atom_site:
_mc = atom_site["pdbx_PDB_model_num"]
_models = (
_mc.as_array(str)
if hasattr(_mc, "as_array")
else np.array(list(_mc), dtype=str) # type: ignore[arg-type]
)
keep &= _models == "1"
if "label_alt_id" in atom_site:
_ac = atom_site["label_alt_id"]
_alts = (
_ac.as_array(str)
if hasattr(_ac, "as_array")
else np.array(list(_ac), dtype=str) # type: ignore[arg-type]
)
keep &= np.isin(_alts, [".", "?", "", "A"])
filtered = _raw[keep]
if len(filtered) == len(structure):
label_asym_ids = filtered.tolist()
# If lengths still don't match, fall back to atom.chain_id
# Get entity information from mmCIF
entity_info = {}
try:
if "entity" in block:
entity_category = block["entity"]
if "id" in entity_category and "type" in entity_category:
entity_ids = entity_category["id"]
entity_types = entity_category["type"]
# Convert CIFColumn to list for iteration
if hasattr(entity_ids, "__iter__") and hasattr(
entity_types, "__iter__"
):
# Type annotation to help pyright understand these are iterable
entity_ids_list = list(entity_ids) # type: ignore
entity_types_list = list(entity_types) # type: ignore
for eid, etype in zip(entity_ids_list, entity_types_list):
entity_info[eid] = etype
except Exception:
pass
# Initialize arrays for flat atom representation
sequence_tokens = []
flat_positions = []
flat_elements = []
flat_names = []
flat_hetero = []
token_to_atoms = []
confidence_scores = []
chain_ids = [] # Track chain IDs for each token
atom_idx = 0
# Group atoms by chain and residue.
# Use label_asym_id (distinct per entity) when available, otherwise
# fall back to biotite's chain_id (auth_asym_id).
chain_residue_groups: dict[str, dict[tuple[int, str], dict]] = {}
for atom_i, atom in enumerate(structure):
chain_id = (
label_asym_ids[atom_i] if label_asym_ids is not None else atom.chain_id
)
res_id = atom.res_id
res_name = atom.res_name
if chain_id not in chain_residue_groups:
chain_residue_groups[chain_id] = {}
# Key by (res_id, res_name) to distinguish residues that share
# the same res_id but have different res_name (e.g. a protein
# residue and a ligand that were on the same auth chain).
res_key = (res_id, res_name)
if res_key not in chain_residue_groups[chain_id]:
chain_residue_groups[chain_id][res_key] = {
"atoms": [],
"res_name": res_name,
"is_hetero": atom.hetero,
}
chain_residue_groups[chain_id][res_key]["atoms"].append(atom)
# Create a mapping from chain_id to numeric indices
chain_id_to_numeric = {
chain_id: idx
for idx, chain_id in enumerate(sorted(chain_residue_groups.keys()))
}
# Process each chain and residue
for chain_id in sorted(chain_residue_groups.keys()):
residues = chain_residue_groups[chain_id]
numeric_chain_id = chain_id_to_numeric[chain_id]
for res_key in sorted(residues.keys()):
residue_data = residues[res_key]
res_name = residue_data["res_name"]
atoms = residue_data["atoms"]
is_hetero = residue_data["is_hetero"]
# Skip water molecules
if res_name == "HOH":
continue
# Determine token name
if not is_hetero and res_name in residue_constants.restype_3to1:
# Standard amino acid
token_name = res_name
elif res_name in ["A", "T", "G", "C", "U", "DA", "DT", "DG", "DC"]:
# Nucleotide
token_name = res_name
else:
# Ligand or other molecule
token_name = res_name
sequence_tokens.append(token_name)
chain_ids.append(
numeric_chain_id
) # Store the numeric chain ID for this token
token_start = atom_idx
# Add all atoms from this residue
for atom in atoms:
flat_positions.append(atom.coord)
# Get element character
element = atom.element
flat_elements.append(element)
# Get atom name
atom_name = atom.atom_name
flat_names.append(atom_name)
# Get hetero flag
hetero_flag = atom.hetero
flat_hetero.append(hetero_flag)
atom_idx += 1
# Record token-to-atom mapping
token_to_atoms.append([token_start, atom_idx])
# Add confidence score (B-factor if available, otherwise 1.0)
bfactor = getattr(atoms[0], "b_factor", 50.0) if atoms else 50.0
confidence_scores.append(min(bfactor / 100.0, 1.0))
# Convert to numpy arrays
if not flat_positions:
# Create minimal arrays if no atoms found
atom_positions = np.zeros((0, 3), dtype=np.float32)
atom_elements = np.zeros(0, dtype=object)
atom_names = np.zeros(0, dtype=object)
atom_hetero = np.zeros(0, dtype=bool)
token_to_atoms_array = np.zeros((len(sequence_tokens), 2), dtype=np.int32)
chain_id_array = (
np.array(chain_ids, dtype=np.int64)
if chain_ids
else np.zeros(len(sequence_tokens), dtype=np.int64)
)
else:
atom_positions = np.array(flat_positions, dtype=np.float32)
atom_elements = np.array(flat_elements, dtype=object)
atom_names = np.array(flat_names, dtype=object)
atom_hetero = np.array(flat_hetero, dtype=bool)
token_to_atoms_array = np.array(token_to_atoms, dtype=np.int32)
chain_id_array = np.array(chain_ids, dtype=np.int64)
confidence_array = np.array(confidence_scores, dtype=np.float32)
# Create metadata using the chain_id_to_numeric mapping
if chain_residue_groups:
chain_lookup = {
numeric_id: chain_id
for chain_id, numeric_id in chain_id_to_numeric.items()
}
else:
chain_lookup = {}
metadata = MolecularComplexMetadata(
entity_lookup=entity_info,
chain_lookup=chain_lookup,
assembly_composition=None,
)
# Set complex ID - if input was a path, use the stem; otherwise use default
if os.path.exists(inp):
complex_id = id or Path(inp).stem
else:
complex_id = id or "complex_from_string"
return cls(
id=complex_id,
sequence=sequence_tokens,
atom_positions=atom_positions,
atom_elements=atom_elements,
token_to_atoms=token_to_atoms_array,
chain_id=chain_id_array,
plddt=confidence_array,
metadata=metadata,
atom_names=atom_names,
atom_hetero=atom_hetero,
)
def _get_entity_mapping(
self,
) -> tuple[dict[str, list[str]], dict[str, int], dict[int, tuple[str, ...]]]:
"""Compute chain→sequence, chain→entity_id, and entity_id→sequence mappings.
Returns:
(chain_sequences, chain_to_entity, entity_sequences)
"""
chain_sequences: dict[str, list[str]] = {}
for token_idx in range(len(self.token_to_atoms)):
chain_id_numeric = self.chain_id[token_idx]
chain_id_str = self.metadata.chain_lookup.get(
int(chain_id_numeric), chr(65 + int(chain_id_numeric))
)
if chain_id_str not in chain_sequences:
chain_sequences[chain_id_str] = []
chain_sequences[chain_id_str].append(self.sequence[token_idx])
sequence_to_entity: dict[tuple[str, ...], int] = {}
chain_to_entity: dict[str, int] = {}
entity_sequences: dict[int, tuple[str, ...]] = {}
entity_id_counter = 1
for chain_id_str, sequence in chain_sequences.items():
seq_tuple = tuple(sequence)
if seq_tuple not in sequence_to_entity:
sequence_to_entity[seq_tuple] = entity_id_counter
entity_sequences[entity_id_counter] = seq_tuple
entity_id_counter += 1
chain_to_entity[chain_id_str] = sequence_to_entity[seq_tuple]
return chain_sequences, chain_to_entity, entity_sequences
def _add_entity_information(
self, cif_file: CIFFile, entity_sequences: dict[int, tuple[str, ...]]
) -> None:
"""Add _entity category to CIF file so OST can identify ligands vs polymers."""
entity_ids: list[str] = []
entity_types: list[str] = []
entity_descriptions: list[str] = []
for eid in sorted(entity_sequences.keys()):
seq = entity_sequences[eid]
entity_ids.append(str(eid))
has_protein = any(t in residue_constants.restype_3to1 for t in seq)
has_na = any(
t in ("A", "T", "G", "C", "U", "DA", "DT", "DG", "DC") for t in seq
)
if has_protein or has_na:
entity_types.append("polymer")
if has_protein:
entity_descriptions.append(f"Polymer entity {eid} (protein)")
else:
entity_descriptions.append(f"Polymer entity {eid} (nucleic acid)")
else:
entity_types.append("non-polymer")
entity_descriptions.append(f"Non-polymer entity {eid}")
if entity_ids:
cif_file.block["entity"] = CIFCategory(
name="entity",
columns={
"id": CIFColumn(
data=CIFData(array=np.array(entity_ids), dtype=np.str_)
),
"type": CIFColumn(
data=CIFData(array=np.array(entity_types), dtype=np.str_)
),
"pdbx_description": CIFColumn(
data=CIFData(array=np.array(entity_descriptions), dtype=np.str_)
),
},
)
# Add _struct_asym to map chain IDs to entity IDs
_, chain_to_entity, _ = self._get_entity_mapping()
if chain_to_entity:
asym_ids = sorted(chain_to_entity.keys())
asym_entity_ids = [str(chain_to_entity[c]) for c in asym_ids]
cif_file.block["struct_asym"] = CIFCategory(
name="struct_asym",
columns={
"id": CIFColumn(
data=CIFData(array=np.array(asym_ids), dtype=np.str_)
),
"entity_id": CIFColumn(
data=CIFData(array=np.array(asym_entity_ids), dtype=np.str_)
),
},
)
def to_mmcif(self) -> str:
"""Write MolecularComplex to mmcif string using biotite.
Returns:
String representation of the complex in mmCIF format
"""
# Pre-allocate AtomArray
n_atoms = len(self.atom_positions)
atom_array = bs.AtomArray(length=n_atoms)
# Set coordinates directly (already vectorized)
atom_array.coord = self.atom_positions
# Pre-allocate per-atom arrays
atom_res_ids = np.zeros(n_atoms, dtype=np.int32)
atom_chain_ids = np.empty(n_atoms, dtype=object)
atom_res_names = np.empty(n_atoms, dtype=object)
atom_hetero = np.zeros(n_atoms, dtype=bool)
atom_bfactors = np.zeros(n_atoms, dtype=np.float32)
atom_names = np.empty(n_atoms, dtype=object)
# Build entity mappings: chains with identical sequences share entity ID
_, chain_to_entity, entity_sequences = self._get_entity_mapping()
atom_entity_ids = np.zeros(n_atoms, dtype=np.int32)
# Track residue IDs per chain
chain_res_counters: dict[int, int] = {}
# Vectorized expansion of token-level to atom-level annotations
for token_idx, (start, end) in enumerate(self.token_to_atoms):
token = self.sequence[token_idx]
chain_id_numeric = self.chain_id[token_idx]
chain_id_str = self.metadata.chain_lookup.get(
int(chain_id_numeric), chr(65 + int(chain_id_numeric))
)
# Track residue numbering per chain
if chain_id_numeric not in chain_res_counters:
chain_res_counters[chain_id_numeric] = 1
res_id = chain_res_counters[chain_id_numeric]
chain_res_counters[chain_id_numeric] += 1
# Determine if protein
is_protein = token in residue_constants.restype_3to1
# Get atom names for this residue
if self.atom_names is not None:
# Use stored atom names (preserves original names from mmCIF)
names = list(self.atom_names[start:end])
elif is_protein:
# Fallback: use standard protein atom names
standard_names = residue_constants.residue_atoms.get(
token, ["N", "CA", "C", "O"]
)
names = standard_names[: end - start]
# Pad if needed
while len(names) < (end - start):
names.append(f"X{len(names)+1}")
else:
# Fallback: generate names for ligands/nucleic acids
names = [f"C{i+1}" for i in range(end - start)]
# Vectorized assignment for this token's atoms
atom_res_ids[start:end] = res_id
atom_chain_ids[start:end] = chain_id_str
atom_res_names[start:end] = token
# Use stored hetero flags if available, otherwise guess based on protein status
if self.atom_hetero is not None:
atom_hetero[start:end] = self.atom_hetero[start:end]
else:
atom_hetero[start:end] = not is_protein
atom_bfactors[start:end] = self.plddt[token_idx] * 100.0
atom_names[start:end] = names
atom_entity_ids[start:end] = chain_to_entity.get(chain_id_str, 1)
# Set all AtomArray attributes at once (convert object arrays to proper string arrays)
# res_name uses U8 to accommodate CCD codes up to 5 characters (e.g., A1AZ2);
# chain_id uses U16 because chain names like ``ligand_1`` / ``ligand_2`` /
# auth-asym ids of arbitrary length are possible.
atom_array.res_id = atom_res_ids
atom_array.chain_id = np.array(atom_chain_ids, dtype="U16")
atom_array.res_name = np.array(atom_res_names, dtype="U8")
atom_array.hetero = atom_hetero
atom_array.atom_name = np.array(atom_names, dtype="U4")
atom_array.add_annotation("b_factor", dtype=float)
atom_array.b_factor = atom_bfactors
atom_array.add_annotation("entity_id", dtype=int)
atom_array.entity_id = atom_entity_ids
# Use existing elements or infer them from atom names
if self.atom_elements is not None and len(self.atom_elements) == n_atoms:
# Convert object array to proper string array for biotite
atom_array.element = np.array(self.atom_elements, dtype="U4")
else:
# Use biotite's built-in element inference
atom_array.element = bs.infer_elements(atom_array)
# Create CIF file and set structure
cif_file = CIFFile()
set_structure(cif_file, atom_array, data_block=self.id)
# Manually fix label_entity_id (biotite doesn't use entity_id annotation correctly)
if "atom_site" in cif_file.block:
atom_site = cif_file.block["atom_site"]
if "label_asym_id" in atom_site and "label_entity_id" in atom_site:
label_asym_ids = atom_site["label_asym_id"]
if hasattr(label_asym_ids, "as_array"):
chain_ids_list = label_asym_ids.as_array(str).tolist()
elif hasattr(label_asym_ids, "__iter__"):
chain_ids_list = list(label_asym_ids) # type: ignore[arg-type]
else:
chain_ids_list = []
updated_entity_ids = [
str(chain_to_entity.get(cid, 1)) for cid in chain_ids_list
]
if updated_entity_ids:
atom_site["label_entity_id"] = CIFColumn(
data=CIFData(array=np.array(updated_entity_ids), dtype=np.str_)
)
# Add _entity category for OST compatibility
self._add_entity_information(cif_file, entity_sequences)
# Convert to string
output = io.StringIO()
cif_file.write(output)
return output.getvalue()
def dockq(self, native: "MolecularComplex") -> Any:
"""Compute DockQ score against native structure.
Args:
native: Native MolecularComplex to compute DockQ against
Returns:
DockQ result containing score and alignment information
"""
# Imports moved to top of file
# Convert both complexes to ProteinComplex format for DockQ computation
# This extracts only the protein portion and converts to PDB format
try:
self_pc = self.to_protein_complex()
native_pc = native.to_protein_complex()
except ValueError as e:
raise ValueError(
f"Cannot convert MolecularComplex to ProteinComplex for DockQ: {e}"
)
# Normalize chain IDs for PDB compatibility
self_pc = self_pc.normalize_chain_ids_for_pdb()
native_pc = native_pc.normalize_chain_ids_for_pdb()
# Use the existing ProteinComplex.dockq() method
try:
dockq_result = self_pc.dockq(native_pc)
return dockq_result
except Exception:
# Fallback to manual DockQ computation if ProteinComplex.dockq() fails
return self._compute_dockq_manual(native)
def _compute_dockq_manual(self, native: "MolecularComplex") -> Any:
"""Manual DockQ computation fallback."""
# Imports moved to top of file
# Convert both complexes to ProteinComplex format
try:
self_pc = self.to_protein_complex()
native_pc = native.to_protein_complex()
except ValueError as e:
raise ValueError(
f"Cannot convert MolecularComplex to ProteinComplex for DockQ: {e}"
)
# Normalize chain IDs for PDB compatibility
self_pc = self_pc.normalize_chain_ids_for_pdb()
native_pc = native_pc.normalize_chain_ids_for_pdb()
# Write temporary PDB files and run DockQ
with TemporaryDirectory() as tdir:
dir_path = Path(tdir)
self_pdb = dir_path / "self.pdb"
native_pdb = dir_path / "native.pdb"
# Write PDB files
self_pc.to_pdb(self_pdb)
native_pc.to_pdb(native_pdb)
# Run DockQ
try:
output = check_output(["DockQ", str(self_pdb), str(native_pdb)])
output_text = output.decode()
# Parse DockQ output
lines = output_text.split("\n")
# Find the total DockQ score
dockq_score = None
for line in lines:
if "Total DockQ" in line:
match = re.search(r"Total DockQ.*: ([\d.]+)", line)
if match:
dockq_score = float(match.group(1))
break
if dockq_score is None:
# Try to find individual DockQ scores
for line in lines:
if line.startswith("DockQ") and ":" in line:
try:
dockq_score = float(line.split(":")[1].strip())
break
except (ValueError, IndexError):
continue
if dockq_score is None:
raise ValueError("Could not parse DockQ score from output")
# Return a simple result structure
return {
"total_dockq": dockq_score,
"raw_output": output_text,
"aligned": self, # Return self as aligned structure
}
except FileNotFoundError:
raise RuntimeError(
"DockQ is not installed. Please install DockQ to use this method."
)
except Exception as e:
raise RuntimeError(f"DockQ computation failed: {e}")
def rmsd(self, target: "MolecularComplex", **kwargs) -> float:
"""Compute RMSD against target structure.
Args:
target: Target MolecularComplex to compute RMSD against
**kwargs: Additional arguments passed to compute_rmsd
Returns:
float: RMSD value between the two structures
"""
# Imports moved to top of file
# Ensure both complexes have the same number of tokens
if len(self) != len(target):
raise ValueError(
f"Complexes must have the same number of tokens: {len(self)} vs {len(target)}"
)
# Extract center positions for each token (using centroid of atoms)
mobile_coords = []
target_coords = []
atom_mask = []
for i in range(len(self)):
# Get atom positions for this token
mobile_start, mobile_end = self.token_to_atoms[i]
target_start, target_end = target.token_to_atoms[i]
# Extract atom positions
mobile_atoms = self.atom_positions[mobile_start:mobile_end]
target_atoms = target.atom_positions[target_start:target_end]
# Check if both tokens have atoms
if len(mobile_atoms) == 0 or len(target_atoms) == 0:
# Skip tokens with no atoms
continue
# For simplicity, use the centroid of atoms as the representative position
mobile_center = mobile_atoms.mean(axis=0)
target_center = target_atoms.mean(axis=0)
mobile_coords.append(mobile_center)
target_coords.append(target_center)
atom_mask.append(True)
if len(mobile_coords) == 0:
raise ValueError("No valid atoms found for RMSD computation")
# Convert to tensors
mobile_tensor = torch.from_numpy(np.stack(mobile_coords, axis=0)).unsqueeze(
0
) # [1, N, 3]
target_tensor = torch.from_numpy(np.stack(target_coords, axis=0)).unsqueeze(
0
) # [1, N, 3]
mask_tensor = torch.tensor(atom_mask, dtype=torch.bool).unsqueeze(0) # [1, N]
# Compute RMSD using existing infrastructure
rmsd_value = compute_rmsd(
mobile=mobile_tensor,
target=target_tensor,
atom_exists_mask=mask_tensor,
reduction="batch",
**kwargs,
)
return float(rmsd_value)
def lddt_ca(self, target: "MolecularComplex", **kwargs) -> float:
"""Compute LDDT score against target structure.
Args:
target: Target MolecularComplex to compute LDDT against
**kwargs: Additional arguments passed to compute_lddt
Returns:
float: LDDT value between the two structures
"""
# Imports moved to top of file
# Ensure both complexes have the same number of tokens
if len(self) != len(target):
raise ValueError(
f"Complexes must have the same number of tokens: {len(self)} vs {len(target)}"
)
# Extract center positions for each token (using centroid of atoms)
mobile_coords = []
target_coords = []
atom_mask = []
for i in range(len(self)):
# Get atom positions for this token
mobile_start, mobile_end = self.token_to_atoms[i]
target_start, target_end = target.token_to_atoms[i]
# Extract atom positions
mobile_atoms = self.atom_positions[mobile_start:mobile_end]
target_atoms = target.atom_positions[target_start:target_end]
# Check if both tokens have atoms
if len(mobile_atoms) == 0 or len(target_atoms) == 0:
# Skip tokens with no atoms
mobile_coords.append(np.full(3, np.nan))
target_coords.append(np.full(3, np.nan))
atom_mask.append(False)
continue
# For simplicity, use the centroid of atoms as the representative position
mobile_center = mobile_atoms.mean(axis=0)
target_center = target_atoms.mean(axis=0)
mobile_coords.append(mobile_center)
target_coords.append(target_center)
atom_mask.append(True)
if not any(atom_mask):
raise ValueError("No valid atoms found for LDDT computation")
# Convert to tensors
mobile_tensor = torch.from_numpy(np.stack(mobile_coords, axis=0)).unsqueeze(
0
) # [1, N, 3]
target_tensor = torch.from_numpy(np.stack(target_coords, axis=0)).unsqueeze(
0
) # [1, N, 3]
mask_tensor = torch.tensor(atom_mask, dtype=torch.bool).unsqueeze(0) # [1, N]
# Compute LDDT using existing infrastructure
lddt_value = compute_lddt(
all_atom_pred_pos=mobile_tensor,
all_atom_positions=target_tensor,
all_atom_mask=mask_tensor,
per_residue=False, # Return overall LDDT score
**kwargs,
)
return float(lddt_value)
def state_dict(self):
"""This state dict is optimized for storage, so it turns things to fp16 whenever
possible and converts numpy arrays to lists for JSON serialization.
"""
dct = {k: v for k, v in vars(self).items()}
for k, v in dct.items():
if isinstance(v, np.ndarray):
match v.dtype:
case np.int64:
dct[k] = v.astype(np.int32).tolist()
case np.float64 | np.float32:
dct[k] = v.astype(np.float16).tolist()
case _:
dct[k] = v.tolist()
elif isinstance(v, MolecularComplexMetadata):
dct[k] = asdict(v)
return dct
def to_blob(self) -> bytes:
return brotli.compress(msgpack.dumps(self.state_dict()), quality=5)
@classmethod
def from_state_dict(cls, dct):
for k, v in dct.items():
if isinstance(v, list) and k in [
"atom_positions",
"atom_elements",
"atom_names",
"atom_hetero",
"token_to_atoms",
"chain_id",
"plddt",
]:
dct[k] = np.array(v)
for k, v in dct.items():
if isinstance(v, np.ndarray):
if k in ["atom_positions", "plddt"]:
dct[k] = v.astype(np.float32)
elif k in ["token_to_atoms", "chain_id"]:
dct[k] = (
v.astype(np.int32)
if k == "token_to_atoms"
else v.astype(np.int64)
)
dct["metadata"] = MolecularComplexMetadata(**dct["metadata"])
# Backward compatibility: if chain_id is missing, create default array
if "chain_id" not in dct:
# Default all tokens to chain 0
dct["chain_id"] = np.zeros(len(dct["sequence"]), dtype=np.int64)
return cls(**dct)
@classmethod
def from_blob(cls, input: Path | str | io.BytesIO | bytes):
match input:
case Path() | str():
bytes = Path(input).read_bytes()
case io.BytesIO():
bytes = input.getvalue()
case _:
bytes = input
return cls.from_state_dict(
msgpack.loads(brotli.decompress(bytes), strict_map_key=False)
)
|